SOTAVerified

Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 511–520 of 1706 papers

TitleStatusHype
Systematic Literature Review on Application of Machine Learning in Continuous Integration—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
Unraveling Cold Start Enigmas in Predictive Analytics for OTT Media: Synergistic Meta-Insights and Multimodal Ensemble Mastery—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text—0
Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
HTPS: Heterogeneous Transferring Prediction System for Healthcare Datasets—0
Catch: Collaborative Feature Set Search for Automated Feature EngineeringCode0
MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified